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Autism spectrum disorder (ASD) is a systemic multi-organ condition characterized by profound biological heterogeneity. Clinical management currently faces a bottleneck due to reliance on subjective behavioral phenotyping rather than objective molecular markers. This review synthesizes breakthroughs in ASD diagnostics, moving from a reductionist “one-size-fits-all” approach toward an integrative systems biology framework. We evaluate how genetic variants and epigenetic modifications converge on shared pathways, such as synaptic homeostasis and chromatin remodeling. Furthermore, we highlight the diagnostic potential of proteomic and metabolomic signatures, which capture functional states more proximally to the clinical phenotype, including sex-specific metabotypes. The roles of neuroinflammation, oxidative stress, and the gut–microbiota–brain axis are delineated as key contributing factors of pathophysiology. Finally, we propose a roadmap for integrating multi-omics data via machine learning to facilitate early screening and precision therapeutic stratification. By bridging molecular footprints with clinical phenotyping, this synthesis provides a foundation for personalized medicine in neurodevelopmental disorders.
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Zheng et al. (2026) studied this question.
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